Yuantao Fan

Papers

Total Citations

H-Index

About

Yuantao Fan is a researcher whose work bridges the fields of autonomous robotics and human-computer interaction, with a particular focus on perception and mapping in structured environments. His most-cited contributions include pioneering methods for infrastructure mapping using micro aerial vehicles (MAVs), where he developed techniques to enable drones to navigate and map well-structured indoor spaces with high precision. This work, published in 2016, has garnered 3 citations and laid groundwork for efficient autonomous inspection and surveying. In parallel, Fan has advanced gesture recognition technology through his research on hand detection and gesture recognition using symmetric patterns, also from 2016, which has similarly earned 3 citations. This contribution addresses key challenges in robust, real-time human-machine interfaces by leveraging geometric symmetries to improve detection accuracy. While his citation counts reflect a focused, early-stage impact, Fan’s dual emphasis on aerial robotics and intuitive interaction systems demonstrates a commitment to creating practical, deployable solutions. His work is particularly relevant for students and researchers interested in the intersection of computer vision, unmanned systems, and user-centered design, offering foundational insights into how autonomous agents can perceive and interact with human environments.